Structured or fielded metadata is the basis for many digital library services, including searching and browsing. Yet, little is known about the impact of using structure on the effectiveness of such services. In this paper, we investigate a key research question: do structured queries improve effectiveness in DL searching? To answer this question, we empirically compared the use of unstructured queries to the use of structured queries. We then tested the capability of a simple Bayesian network system, built on top of a DL retrieval engine, to infer the best structured queries from the keywords entered by the user. Experiments performed with 20 subjects working with a DL containing a large collection of computer science literature clearly indicate that structured queries, either manually constructed or automatically generated, perform better than their unstructured counterparts, in the majority of cases. Also, automatic structuring of queries appears to be an effective and viable alternative to manual structuring that may significantly reduce the burden on users.
Abstract. In this paper we discuss the problem of handling many classification schemes within the context of a single digital library concurrently, which we term multischeming. We discuss how to represent which category describes an object in the digital library in this system, as well as the workings of the browsing process which is performed by the user. We motivate this problem as related to digital library interoperability, and propose an architecture for representation of classification schemes in the digital library which solves the problem. We also discuss its implementation in the CITIDEL project.
In this article we present an evaluation of text clustering and classification methods for creating digital library browse interfaces, focusing on the particular case of collections made up of heterogeneous metadata records. This situation is common in "portal" style digital libraries, which are built by harvesting content from many disparate sources, typically using the Open Archives Protocol for Metadata Harvesting (OAI-PMH). By studying the activity of users in an experimental system, we find that taxonomies built or populated using machine-learning (or "AI") techniques provide a potentially useful avenue for browsing in this digital library scenario.
This article defines the digital library setting as it relates to commons-based peer production (CBPP) [1]. Motivations for selecting the CBPP method in this setting will be discussed, and the challenges of CBPP will be described. The Noosphere system will be presented as a case study to demonstrate CBPP digital library system design. Specific aspects addressed include: how an "economy of ideas" is the basis for productive activity in Noosphere, how logical integration of content is performed, how opportunistic updating is attained, what services Noosphere provides to foster community and provide for social integration, and what could be done to improve the system. Also discussed are different ways to benefit from commons-based peer production in digital libraries.
In this paper, we describe an experiment in combined searching of web pages and digital library resources, exposed via an Open Archives metadata provider and web gateway service. We utilize only free/open source software components for our investigation, in order to demonstrate feasibility of deployment for all institutions.
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